# make_friedman1#

sklearn.datasets.make_friedman1(n_samples=100, n_features=10, *, noise=0.0, random_state=None)[source]#

Generate the “Friedman #1” regression problem.

This dataset is described in Friedman [1] and Breiman [2].

Inputs `X` are independent features uniformly distributed on the interval [0, 1]. The output `y` is created according to the formula:

```y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).
```

Out of the `n_features` features, only 5 are actually used to compute `y`. The remaining features are independent of `y`.

The number of features has to be >= 5.

Read more in the User Guide.

Parameters:
n_samplesint, default=100

The number of samples.

n_featuresint, default=10

The number of features. Should be at least 5.

noisefloat, default=0.0

The standard deviation of the gaussian noise applied to the output.

random_stateint, RandomState instance or None, default=None

Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:
Xndarray of shape (n_samples, n_features)

The input samples.

yndarray of shape (n_samples,)

The output values.

References

[1]

J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.

[2]

L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.

Examples

```>>> from sklearn.datasets import make_friedman1
>>> X, y = make_friedman1(random_state=42)
>>> X.shape
(100, 10)
>>> y.shape
(100,)
>>> list(y[:3])
[np.float64(16.8...), np.float64(5.8...), np.float64(9.4...)]
```